Fluid accumulation and major adverse kidney events in sepsis: a multicenter observational study

Background Whether early fluid accumulation is a risk factor for adverse renal outcomes in septic intensive care unit (ICU) patients remains uncertain. We assessed the association between cumulative fluid balance and major adverse kidney events within 30 days (MAKE30), a composite of death, dialysis, or sustained renal dysfunction, in such patients. Methods We performed a multicenter, retrospective observational study in 1834 septic patients admitted to five ICUs in three hospitals in Stockholm, Sweden. We used logistic regression analysis to assess the association between cumulative fluid balance during the first two days in ICU and subsequent risk of MAKE30, adjusted for demographic factors, comorbidities, baseline creatinine, illness severity variables, haemodynamic characteristics, chloride exposure and nephrotoxic drug exposure. We assessed the strength of significant exposure variables using a relative importance analysis. Results Overall, 519 (28.3%) patients developed MAKE30. Median (IQR) cumulative fluid balance was 5.3 (2.8–8.1) l in the MAKE30 group and 4.1 (1.9–6.8) l in the no MAKE30 group, with non-resuscitation fluids contributing to approximately half of total fluid input in each group. The adjusted odds ratio for MAKE30 was 1.05 (95% CI 1.02–1.09) per litre cumulative fluid balance. On relative importance analysis, the strongest factors regarding MAKE30 were, in decreasing order, baseline creatinine, cumulative fluid balance, and age. In the secondary outcome analysis, the adjusted odds ratio for dialysis or sustained renal dysfunction was 1.06 (95% CI 1.01–1.11) per litre cumulative fluid balance. On separate sensitivity analyses, lower urine output and early acute kidney injury, respectively, were independently associated with MAKE30, whereas higher fluid input was not. Conclusions In ICU patients with sepsis, a higher cumulative fluid balance after 2 days in ICU was associated with subsequent development of major adverse kidney events within 30 days, including death, renal replacement requirement, or persistent renal dysfunction. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-022-01040-6.

Exposure variables collected at baseline or during exposure period. Filtering and processing of mean arterial pressure data Data Collection The mean arterial pressure (MAP) was measured via an indwelling arterial line. MAP measurements were automatically transferred to the Centricity Critical Care electronic patient data management system in intervals of 2 minutes on average. Pre-filtering 1. Data points with MAP below 20 mmHg or above 250 mmHg were considered outliers due to artifacts and removed. 2. Patients with less than 50 recordings (between 20 and 250 mmHg) were not considered. 3. The time stamp of the first recording of each patient was set to be time 0 (zero) min. Filtering For each recoding r of each patient: 1. A window w of 20 minutes around the recording (10 minutes before and 10 minutes after) was considered. If there were less than 10 minutes before or after the recording, the maximum amount of available minutes were considered. 2. The median MAP m of the recordings in the window w was calculated. 3. The median absolute deviation (MAD) of the recordings in the window w was calculated. The MAD is the median absolute distance between the recordings in the window w and the median MAP m. 4. If the absolute distance between the recording r and the median MAP m was 4 times bigger than the MAD, the recording r was considered an outlier and removed. Processing After filtering, the cumulative time that a patient's MAP was below 65 mmHg was calculated.
• If there were no recordings between two recordings with less than 65 mmHg, it was assumed the patient remained with a MAP below 65 mmHg for the time missing.
• The MAP between recordings below and above 65 mmHg was linearly interpolated to smooth the cumulative time.

Description of Variable Importance analysis
In a classification analysis such using the recursive partitioning function implemented in the 'rpart' R package (Therneau et al. 2017), the algorithm tries to find a rule c(x) consisting of binary splits that sorts the input population into different groups/classes.
In order to find the best splits, a loss function is minimized. The loss function measures how many observations have been classified correctly. Note that a variable x_i, i=1,…,n can be split several times. An overall measure of variable importance is the sum of the goodness of split measures (obtained with the loss function) for each split for which it was the primary variable, plus goodness * (adjusted agreement) for all splits in which it was a surrogate (=help to account for missing values). In order to calculate the relative importance, the importance of each variable was divided by the total importance obtained by summing over all variables considered.
The loss function is also called 0-1 loss function in this context since it is based on a sum of zeros and ones: 1 if the observation was correctly classified and 0 if not. If the sum is divided by the number of observation it is also referred to as error rate.